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Total or partial replacement of traditional durum wheat semolina (DWS) by alternative flours, such as legumes or wholegrain cereals in pasta improves their nutritional quality and can make them interesting vector for fortification. Climate-smart gluten-free (C-GF) flours, such as legumes (bambara groundnut, chickpea, cowpea, faba bean, and pigeon pea), some cereals (amaranth, teff, millet, and sorghum), and tubers (cassava and orange fleshed sweet potato), are of high interest to face ecological transition and develop sustainable food systems. In this review, an overview and a critical analysis of their nutritional potential for pasta production and processing conditions are undertaken. Special emphasis is given to understanding the influence of formulation and processing on techno-functional and nutritional (starch and protein digestibility) properties. Globally C-GF flours improve pasta protein quantity and quality, fibers, and micronutrients contents while keeping a low glycemic index and increasing protein digestibility. However, their use introduces anti-nutritional factors and could lead to the alteration of their techno-functional properties (higher cooking losses, lower firmness, and variability in color in comparison to classical DWS pasta). Nevertheless, these alternative pasta remain more interesting in terms of nutritional and techno-functional quality than traditional maize and rice-based gluten free pasta.

Drug-target interactions (DTIs) prediction algorithms are used at various stages of the drug discovery process. In this context, specific problems such as deorphanization of a new therapeutic target or target identification of a drug candidate arising from phenotypic screens require large-scale predictions across the protein and molecule spaces. DTI prediction heavily relies on supervised learning algorithms that use known DTIs to learn associations between molecule and protein features, allowing for the prediction of new interactions based on learned patterns. The algorithms must be broadly applicable to enable reliable predictions, even in regions of the protein or molecule spaces where data may be scarce. In this paper, we address two key challenges to fulfill these goals: building large, high-quality training datasets and designing prediction methods that can scale, in order to be trained on such large datasets. First, we introduce LCIdb, a curated, large-sized dataset of DTIs, offering extensive coverage of both the molecule and druggable protein spaces. Notably, LCIdb contains a much higher number of molecules than publicly available benchmarks, expanding coverage of the molecule space. Second, we propose Komet (Kronecker Optimized METhod), a DTI prediction pipeline designed for scalability without compromising performance. Komet leverages a three-step framework, incorporating efficient computation choices tailored for large datasets and involving the Nyström approximation. Specifically, Komet employs a Kronecker interaction module for (molecule, protein) pairs, which efficiently captures determinants in DTIs, and whose structure allows for reduced computational complexity and quasi-Newton optimization, ensuring that the model can handle large training sets, without compromising on performance. Our method is implemented in open-source software, leveraging GPU parallel computation for efficiency. We demonstrate the interest of our pipeline on various datasets, showing that Komet displays superior scalability and prediction performance compared to state-of-the-art deep learning approaches. Additionally, we illustrate the generalization properties of Komet by showing its performance on an external dataset, and on the publicly available L H benchmark designed for scaffold hopping problems. Komet is available open source at https://komet.readthedocs.io and all datasets, including LCIdb, can be found at https://zenodo.org/records/10731712.

BACKGROUND: Risk stratification and therapeutic decision-making for myelodysplastic syndromes (MDS) are based on the International Prognostic Scoring System–Revised (IPSS-R), which considers hematologic parameters and cytogenetic abnormalities. Somatic gene mutations are not yet used in the risk stratification of patients with MDS. METHODS: To develop a clinical-molecular prognostic model (IPSS-Molecular [IPSS-M]), pretreatment diagnostic or peridiagnostic samples from 2957 patients with MDS were profiled for mutations in 152 genes. Clinical and molecular variables were evaluated for associations with leukemia-free survival, leukemic transformation, and overall survival. Feature selection was applied to determine the set of independent IPSS-M prognostic variables. The relative weights of the selected variables were estimated using a robust Cox multivariable model adjusted for confounders. The IPSS-M was validated in an external cohort of 754 Japanese patients with MDS. RESULTS: We mapped at least one oncogenic genomic alteration in 94% of patients with MDS. Multivariable analysis identified TP53multihit, FLT3 mutations, and MLLPTD as top genetic predictors of adverse outcomes. Conversely, SF3B1 mutations were associated with favorable outcomes, but this was modulated by patterns of comutation. Using hematologic parameters, cytogenetic abnormalities, and somatic mutations of 31 genes, the IPSS-M resulted in a unique risk score for individual patients. We further derived six IPSS-M risk categories with prognostic differences. Compared with the IPSS-R, the IPSS-M improved prognostic discrimination across all clinical end points and restratified 46% of patients. The IPSS-M was applicable in primary and secondary/therapy-related MDS. To simplify clinical use of the IPSS-M, we developed an open-access Web calculator that accounts for missing values. CONCLUSIONS: Combining genomic profiling with hematologic and cytogenetic parameters, the IPSS-M improves the risk stratification of patients with MDS and represents a valuable tool for clinical decision-making. (Funded by Celgene Corporation through the MDS Foundation, the Josie Robertson Investigators Program, the Edward P. Evans Foundation, the Projects of National Relevance of the Italian Ministry of University and Research, Associazione Italiana per la Ricerca sul Cancro, the Japan Agency for Medical Research and Development, Cancer Research UK, the Austrian Science Fund, the MEXT [Japanese Ministry of Education, Culture, Sports, Science and Technology] Program for Promoting Research on the Supercomputer Fugaku, the Japan Society for the Promotion of Science, the Taiwan Department of Health, and Celgene Corporation through the MDS Foundation.)

Functional magnetic resonance imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. Contrariwise, recent data-collection efforts have started to target a systematic spatial representation of multiple mental functions. In this paper, we leverage the Individual Brain Charting (IBC) dataset-a high-resolution task-fMRI dataset acquired in a fixed environment-in order to study the feasibility of individual mapping. First, we verify that the IBC brain maps reproduce those obtained from previous, large-scale datasets using the same tasks. Second, we confirm that the elementary spatial components, inferred across all tasks, are consistently mapped within and, to a lesser extent, across participants. Third, we demonstrate the relevance of the topographic information of the individual contrast maps, showing that contrasts from one task can be predicted by contrasts from other tasks. At last, we showcase the benefit of contrast accumulation for the fine functional characterization of brain regions within a prespecified network. To this end, we analyze the cognitive profile of functional territories pertaining to the language network and prove that these profiles generalize across participants., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

More than two decades of functional magnetic resonance imaging (fMRI) of the human brain have succeeded to identify, with a growing level of precision, the neural basis of multiple cognitive skills within various domains (perception, sensorimotor processes, language, emotion and social cognition …). Progress has been made in the comprehension of the functional organization of localized brain areas. However, the long time required for fMRI acquisition limits the number of experimental conditions performed in a single individual. As a consequence, distinct brain localizations have mostly been studied in separate groups of participants, and their functional relationships at the individual level remain poorly understood. To address this issue, we report here preliminary results on a database of fMRI data acquired on 78 individuals who each performed a total of 29 experimental conditions, grouped in 4 cross-domains functional localizers. This protocol has been designed to efficiently isolate, in a single session, the brain activity associated with language, numerical representation, social perception and reasoning, premotor and visuomotor representations. Analyses are reported at the group and at the individual level, to establish the ability of our protocol to selectively capture distinct regions of interest in a very short time. Test-retest reliability was assessed in a subset of participants. The activity evoked by the different contrasts of the protocol is located in distinct brain networks that, individually, largely replicate previous findings and, taken together, cover a large proportion of the cortical surface. We provide detailed analyses of a subset of regions of relevance: the left frontal, left temporal and middle frontal cortices. These preliminary analyses highlight how combining such a large set of functional contrasts may contribute to establish a finer-grained brain atlas of cognitive functions, especially in regions of high functional overlap. Detailed structural images (structural connectivity, micro-structures, axonal diameter) acquired in the same individuals in the context of the ARCHI database provide a promising situation to explore functional/structural interdependence. Additionally, this protocol might also be used as a way to establish individual neurofunctional signatures in large cohorts., (Copyright © 2019. Published by Elsevier Inc.)

Cognitive performance is highly heritable. However, little is known about common genetic influences on cognitive ability and brain activation when engaged in a cognitive task. The Human Connectome Project (HCP) offers a unique opportunity to study this shared genetic etiology with an extended pedigree of 785 individuals. To investigate this common genetic origin, we took advantage of the HCP dataset, which includes both language and mathematics activation tasks. Using the HCP multimodal parcellation, we identified areals in which inter-individual functional MRI (fMRI) activation variance was significantly explained by genetics. Then, we performed bivariate genetic analyses between the neural activations and behavioral scores, corresponding to the fMRI task accuracies, fluid intelligence, working memory and language performance. We observed that several parts of the language network along the superior temporal sulcus, as well as the angular gyrus belonging to the math processing network, are significantly genetically correlated with these indicators of cognitive performance. This shared genetic etiology provides insights into the brain areas where the human-specific genetic repertoire is expressed. Studying the association of polygenic risk scores, using variants associated with human cognitive ability and brain activation, would provide an opportunity to better understand where these variants are influential.

Functional Magnetic Resonance Imaging (fMRI) has furthered brain mapping on perceptual, motor, as well as higher-level cognitive functions. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a cohort of 12 participants performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The present article gives a detailed description of the first release of the IBC dataset. It comprises a dozen of tasks, addressing both low- and high- level cognitive functions. This openly available dataset is thus intended to become a reference for cognitive brain mapping.

The Brainomics/Localizer database exposes part of the data collected by the in-house Localizer project, which planned to acquire four types of data from volunteer research subjects: anatomical MRI scans, functional MRI data, behavioral and demographic data, and DNA sampling. Over the years, this local project has been collecting such data from hundreds of subjects. We had selected 94 of these subjects for their complete datasets, including all four types of data, as the basis for a prior publication; the Brainomics/Localizer database publishes the data associated with these 94 subjects. Since regulatory rules prevent us from making genetic data available for download, the database serves only anatomical MRI scans, functional MRI data, behavioral and demographic data. To publish this set of heterogeneous data, we use dedicated software based on the open-source CubicWeb semantic web framework. Through genericity in the data model and flexibility in the display of data (web pages, CSV, JSON, XML), CubicWeb helps us expose these complex datasets in original and efficient ways., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Two areas of the occipitotemporal cortex show a remarkable hemispheric lateralization: written words activate the visual word form area (VWFA) in the left fusiform gyrus and faces activate a symmetrical site in the right hemisphere, the fusiform face area (FFA). While the lateralization of the VWFA fits with the leftward asymmetry of the speech processing network, origin of the rightward asymmetry for faces is still unclear. Using fMRI data from 64 subjects (including 16 monozygotic (MZ) and 13 dizygotic (DZ) twin pairs), we investigated how activations evoked by written words, faces, and spoken language are co-lateralized in the temporal lobe, and whether this organization reflects genetic factors or individual reading expertise. We found that the lateralization of the left superior temporal activation for spoken language correlates with the lateralization of occipitotemporal activations for both written words and faces. Behavioral reading scores also modulate the responses to words and faces. Estimation of genetic and environmental contributions shows that activations of the VWFA, the occipital face area, and the temporal speech areas are partially under genetic control whereas activation of the FFA is primarily influenced by individual experience. Our results stress the importance of both genetic factors and acquired expertise in the occipitotemporal organization., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Inter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes.

Twin studies have long suggested a genetic influence on inter-individual variations in mathematical abilities, and candidate genes have been identified by genome-wide association studies. However, the localization of the brain regions under genetic influence during number manipulation is still unexplored. Here we investigated fMRI data from a group of 19 MZ (monozygotic) and 13 DZ (dizygotic) adult twin pairs, scanned during a mental calculation task. We examined both the activation and the degree of functional lateralization in regions of interest (ROIs) centered on the main activated peaks. Heritability was first investigated by comparing the respective MZ and DZ correlations. Then, genetic and environmental contributions were jointly estimated by fitting a ACE model classically used in twin studies. We found that a subset of the activated network was under genetic influence, encompassing the bilateral posterior superior parietal lobules (PSPL), the right intraparietal sulcus (IPS) and a left superior frontal region. An additional region of the left inferior parietal cortex (IPC), whose deactivation correlated with a behavioral calculation score, also presented higher similarity between MZ than between DZ twins, thus offering a plausible physiological basis for the observable inheritance of math scores. Finally, the main impact of the shared environment was found in the lateralization of activation within the intraparietal sulcus. These maps of genetic and environmental contributions provide precise candidate phenotypes for further genetic association analyses, and illuminate how genetics and education shape the development of number processing networks., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Functional Magnetic Resonance Imaging (fMRI) studies map the human brain by testing the response of groups of individuals to carefully-crafted and contrasted tasks in order to delineate specialized brain regions and networks. The number of functional networks extracted is limited by the number of subject-level contrasts and does not grow with the cohort. Here, we introduce a new group-level brain mapping strategy to differentiate many regions reflecting the variety of brain network configurations observed in the population. Based on the principle of functional segregation, our approach singles out functionally-specialized brain regions by learning group-level functional profiles on which the response of brain regions can be represented sparsely. We use a dictionary-learning formulation that can be solved efficiently with on-line algorithms, scaling to arbitrary large datasets. Importantly, we model inter-subject correspondence as structure imposed in the estimated functional profiles, integrating a structure-inducing regularization with no additional computational cost. On a large multi-subject study, our approach extracts a large number of brain networks with meaningful functional profiles.

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate an exploratory multivariate method in order to identify a set of Single Nucleotide Polymorphisms (SNPs) covarying with a set of neuroimaging phenotypes derived from functional Magnetic Resonance Imaging (fMRI). Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics. Here, we propose to transpose this idea to the DNA vs. imaging context. However, in very high-dimensional settings like in imaging genetics studies, such multivariate methods may encounter overfitting issues. Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes computed from reading and speech comprehension contrast maps. We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure. Univariate selection appears to be necessary to reduce the dimensionality. However, the significant association uncovered by this two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful genetic associations calls for a multivariate approach., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Regions of human ventral extrastriate visual cortex develop specializations for natural categories (e.g., faces) and cultural artifacts (e.g., words). In adults, category-based specializations manifest as greater neural responses in visual regions of the brain (e.g., fusiform gyrus) to some categories over others. However, few studies have examined how these specializations originate in the brains of children. Moreover, it is as yet unknown whether the development of visual specializations hinges on "increases" in the response to the preferred categories, "decreases" in the responses to nonpreferred categories, or "both." This question is relevant to a long-standing debate concerning whether neural development is driven by building up or pruning back representations. To explore these questions, we measured patterns of visual activity in 4-year-old children for 4 categories (faces, letters, numbers, and shoes) using functional magnetic resonance imaging. We report 2 key findings regarding the development of visual categories in the brain: 1) the categories "faces" and "symbols" doubly dissociate in the fusiform gyrus before children can read and 2) the development of category-specific responses in young children depends on cortical responses to nonpreferred categories that decrease as preferred category knowledge is acquired.

Eighty years ago, the Austrian neurologist Josef Gerstmann observed in a few patients a concomitant impairment in discriminating their own fingers, writing by hand, distinguishing left from right and performing calculations. He claimed that this tetrad of symptoms constituted a syndromal entity, assigned it to a lesion of the dominant parietal lobe and suggested that it was due to damage of a common functional denominator. Ever since, these claims have been debated and an astute synopsis and sceptical discussion was presented 40 years ago by MacDonald Critchley in this journal. Nonetheless, Gerstmann's syndrome has continued to intrigue both clinical neurologists and researchers in neuropsychology, and more frequently than not is described in textbooks as an example of parietal lobe damage. In this review, we revisit the chequered history of this syndrome, which can be seen as a case study of the dialectic evolution of concepts in neuropsychology. In light of several modern era findings of pure cases we conclude that it is legitimate to label the conjunction of symptoms first described by Gerstmann as a 'syndrome', but that it is very unlikely that damage to the same population of cortical neurons should account for all of the four symptoms. Instead, we propose that a pure form of Gerstmann's syndrome might arise from disconnection, via a lesion, to separate but co-localized fibre tracts in the subcortical parietal white matter, a hypothesis for which we have recently provided evidence using combined imaging of functional and structural organization in the healthy brain.

Language and arithmetic are both lateralized to the left hemisphere in the majority of right-handed adults. Yet, does this similar lateralization reflect a single overall constraint of brain organization, such an overall "dominance" of the left hemisphere for all linguistic and symbolic operations? Is it related to the lateralization of specific cerebral subregions? Or is it merely coincidental? To shed light on this issue, we performed a "colateralization analysis" over 209 healthy subjects: We investigated whether normal variations in the degree of left hemispheric asymmetry in areas involved in sentence listening and reading are mirrored in the asymmetry of areas involved in mental arithmetic. Within the language network, a region-of-interest analysis disclosed partially dissociated patterns of lateralization, inconsistent with an overall "dominance" model. Only two of these areas presented a lateralization during sentence listening and reading which correlated strongly with the lateralization of two regions active during calculation. Specifically, the profile of asymmetry in the posterior superior temporal sulcus during sentence processing covaried with the asymmetry of calculation-induced activation in the intraparietal sulcus, and a similar colateralization linked the middle frontal gyrus with the superior posterior parietal lobule. Given recent neuroimaging results suggesting a late emergence of hemispheric asymmetries for symbolic arithmetic during childhood, we speculate that these colateralizations might constitute developmental traces of how the acquisition of linguistic symbols affects the cerebral organization of the arithmetic network.

Objective: To examine the functional neuroanatomy that could account for pure Gerstmann syndrome, which is the selective association of acalculia, finger agnosia, left-right disorientation, and agraphia., Methods: We used structural and functional neuroimaging at high spatial resolution in healthy subjects to seek a shared cortical substrate of the Grundstörung posited by Gerstmann, ie, a common functional denominator accounting for this clinical tetrad. We construed a functional activation paradigm that mirrors each of the four clinical deficits in Gerstmann syndrome and determined cortical activation patterns. We then applied fiber tracking to diffusion tensor images and used cortical activation foci in the four functional domains as seed regions., Results: None of the subjects showed parietal overlap of cortical activation patterns from the four cognitive domains. In every subject, however, the parietal activation patterns across all four domains consistently connected to a small region of subcortical parietal white matter at a location that is congruent with the lesion in a well-documented case of pure Gerstmann syndrome., Interpretation: Our functional neuroimaging findings are not in agreement with Gerstmann's postulate of damage to a common cognitive function underpinning clinical semiology. Our evidence from intact functional neuroanatomy suggests that pure forms of Gerstmann's tetrad do not arise from lesion to a shared cortical substrate but from intraparietal disconnection after damage to a focal region of subcortical white matter.

As literate adults, we appreciate numerical values as abstract entities that can be represented by a numeral, a word, a number of lines on a scorecard, or a sequence of chimes from a clock. This abstract, notation-independent appreciation of numbers develops gradually over the first several years of life. Here, using functional magnetic resonance imaging, we examine the brain mechanisms that 6- and 7-year-old children and adults recruit to solve numerical comparisons across different notation systems. The data reveal that when young children compare numerical values in symbolic and nonsymbolic notations, they invoke the same network of brain regions as adults including occipito-temporal and parietal cortex. However, children also recruit inferior frontal cortex during these numerical tasks to a much greater degree than adults. Our data lend additional support to an emerging consensus from adult neuroimaging, nonhuman primate neurophysiology, and computational modeling studies that a core neural system integrates notation-independent numerical representations throughout development but, early in development, higher-order brain mechanisms mediate this process.

Understanding brain structure and function entails the inclusion of anatomical and functional information in a common space, in order to study how these different informations relate to each other in a population of subjects. In this paper, we revisit the parcellation model and explicitly combine anatomical features, i.e. a segmentation of the cortex into gyri, with a functional information under the form of several cortical maps, which are used to further subdivide the gyri into functionally consistent regions. A probabilistic model is introduced, and the parcellation model is estimated using a Variational Bayes approach. The number of regions in the model is validated based on cross-validation. It is found that about 250 patches of cortex can be delineated both in the left and right hemisphere based on this procedure.

Background: Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level., Results: 81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects., Conclusion: This collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes.

Group studies of functional magnetic resonance imaging datasets are usually based on the computation of the mean signal across subjects at each voxel (random effects analyses), assuming that all subjects have been set in the same anatomical space (normalization). Although this approach allows for a correct specificity (rate of false detections), it is not very efficient for three reasons: i) its underlying hypotheses, perfect coregistration of the individual datasets and normality of the measured signal at the group level are frequently violated; ii) the group size is small in general, so that asymptotic approximations on the parameters distributions do not hold; iii) the large size of the images requires some conservative strategies to control the false detection rate, at the risk of increasing the number of false negatives. Given that it is still very challenging to build generative or parametric models of intersubject variability, we rely on a rule based, bottom-up approach: we present a set of procedures that detect structures of interest from each subject's data, then search for correspondences across subjects and outline the most reproducible activation regions in the group studied. This framework enables a strict control on the number of false detections. It is shown here that this analysis demonstrates increased validity and improves both the sensitivity and reliability of group analyses compared with standard methods. Moreover, it directly provides information on the spatial position correspondence or variability of the activated regions across subjects, which is difficult to obtain in standard voxel-based analyses.

The aim of group fMRI studies is to relate contrasts of tasks or stimuli to regional brain activity increases. These studies typically involve 10 to 16 subjects. The average regional activity statistical significance is assessed using the subject to subject variability of the effect (random effects analyses). Because of the relatively small number of subjects included, the sensitivity and reliability of these analyses is questionable and hard to investigate. In this work, we use a very large number of subject (more than 80) to investigate this issue. We take advantage of this large cohort to study the statistical properties of the inter-subject activity and focus on the notion of reproducibility by bootstrapping. We asked simple but important methodological questions: Is there, from the point of view of reliability, an optimal statistical threshold for activity maps? How many subjects should be included in group studies? What method should be preferred for inference? Our results suggest that i) optimal thresholds can indeed be found, and are rather lower than usual corrected for multiple comparison thresholds, ii) 20 subjects or more should be included in functional neuroimaging studies in order to have sufficient reliability, iii) non-parametric significance assessment should be preferred to parametric methods, iv) cluster-level thresholding is more reliable than voxel-based thresholding, and v) mixed effects tests are much more reliable than random effects tests. Moreover, our study shows that inter-subject variability plays a prominent role in the relatively low sensitivity and reliability of group studies.

Activation of the horizontal segment of the intraparietal sulcus (hIPS) has been observed in various number-processing tasks, whether numbers were conveyed by symbolic numerals (digits, number words) or by nonsymbolic displays (dot patterns). This suggests an abstract coding of numerical magnitude. Here, we critically tested this hypothesis using fMRI adaptation to demonstrate notation-independent coding of numerical quantity in the hIPS. Once subjects were adapted either to dot patterns or to Arabic digits, activation in the hIPS and in frontal regions recovered in a distance-dependent fashion whenever a new number was presented, irrespective of notation changes. This remained unchanged when analyzing the hIPS peaks from an independent localizer scan of mental calculation. These results suggest an abstract coding of approximate number common to dots, digits, and number words. They support the idea that symbols acquire meaning by linking neural populations coding symbol shapes to those holding nonsymbolic representations of quantities.

Inferring the position of functionally active regions from a multi-subject fMRI dataset involves the comparison of the individual data and the inference of a common activity model. While voxel-based analyzes, e.g. Random Effect statistics, are widely used, they do not model each individual activation pattern. Here, we develop a new procedure that extracts structures individually and compares them at the group level. For inference about spatial locations of interest, a Dirichlet Process Mixture Model is used. Finally, inter-subject correspondences are computed with Bayesian Network models. We show the power of the technique on both simulated and real datasets and compare it with standard inference techniques.

The analysis of functional magnetic resonance imaging (fMRI) data recorded on several subjects resorts to the so-called spatial normalization in a common reference space. This normalization is usually carried out on a voxel-by-voxel basis, assuming that after coregistration of the functional images with an anatomical template image in the Talairach reference system, a correct voxel-based inference can be carried out across subjects. Shortcomings of such approaches are often dealt with by spatially smoothing the data to increase the overlap between subject-specific activated regions. This procedure, however, cannot adapt to each anatomo-functional subject configuration. We introduce a novel technique for intra-subject parcellation based on spectral clustering that delineates homogeneous and connected regions. We also propose a hierarchical method to derive group parcels that are spatially coherent across subjects and functionally homogeneous. We show that we can obtain groups (or cliques) of parcels that well summarize inter-subject activations. We also show that the spatial relaxation embedded in our procedure improves the sensitivity of random-effect analysis.

Background: Adaptive game software has been successful in remediation of dyslexia. Here we describe the cognitive and algorithmic principles underlying the development of similar software for dyscalculia. Our software is based on current understanding of the cerebral representation of number and the hypotheses that dyscalculia is due to a "core deficit" in number sense or in the link between number sense and symbolic number representations., Methods: "The Number Race" software trains children on an entertaining numerical comparison task, by presenting problems adapted to the performance level of the individual child. We report full mathematical specifications of the algorithm used, which relies on an internal model of the child's knowledge in a multidimensional "learning space" consisting of three difficulty dimensions: numerical distance, response deadline, and conceptual complexity (from non-symbolic numerosity processing to increasingly complex symbolic operations)., Results: The performance of the software was evaluated both by mathematical simulations and by five weeks of use by nine children with mathematical learning difficulties. The results indicate that the software adapts well to varying levels of initial knowledge and learning speeds. Feedback from children, parents and teachers was positive. A companion article describes the evolution of number sense and arithmetic scores before and after training., Conclusion: The software, open-source and freely available online, is designed for learning disabled children aged 5-8, and may also be useful for general instruction of normal preschool children. The learning algorithm reported is highly general, and may be applied in other domains.

Models of the "visual word form system" postulate that a left occipitotemporal region implements the automatic visual word recognition required for efficient reading. This theory was assessed in a patient in whom reading was explored with behavioral measures, fMRI, and intracranial local field potentials. Prior to surgery, when reading was normal, fMRI revealed a normal mosaic of ventral visual selectivity for words, faces, houses, and tools. Intracranial recordings demonstrated that the left occipitotemporal cortex responded with a short latency to conscious but also to subliminal words. Surgery removed a small portion of word-responsive occipitotemporal cortex overlapping with the word-specific fMRI activation. The patient developed a marked reading deficit, while recognition of other visual categories remained intact. Furthermore, in the post-surgery fMRI map of visual cortex, only word-specific activations disappeared. Altogether, these results provide direct evidence for the causal role of the left occipitotemporal cortex in the recognition of visual words.

FMRI group studies are usually based on stereotactic spatial normalization and present voxel by voxel average activity across subjects. This technique does not in general adequately model the inter subject spatial variability. In this work, we propose to identify functional landmarks that are reliable across subjects with subject specific Talairach coordinates that are similar -but not exactly identical- between subjects. We call these Brain Functional Landmarks (BFLs), and define them based on cross-validation techniques using 38 subjects. We explore a dataset acquired while subjects were involved in several cognitive and sensori-motor processes, and show that this representation allows to classify subjects into sub-groups on the basis of their BFL activity.

Number, like color or movement, is a basic property of the environment. Recently, single neurons tuned to number have been observed in animals. We used both psychophysics and neuroimaging to examine whether a similar neural coding scheme is present in humans. When participants viewed sets of items with a variable number, the bilateral intraparietal sulci responded selectively to number change. Functionally, the shape of this response indicates that humans, like other animal species, encode approximate number on a compressed internal scale. Anatomically, the intraparietal site coding for number in humans is compatible with that observed in macaque monkeys. Our results therefore suggest an evolutionary basis for human elementary arithmetic.

How are comparative judgments performed in the human brain? We scanned subjects with fMRI while they compared stimuli for size, luminance, or number. Regions involved in comparative judgments were identified using three criteria: task-related activation, presence of a distance effect, and interference of one dimension onto the other. We observed considerable overlap in the neural substrates of the three comparison tasks. Interestingly, the amount of overlap predicted the amount of cross-dimensional interference: in both behavior and fMRI, number interfered with size, and size with luminance, but number did not interfere with luminance. The results suggest that during comparative judgments, the relevant continuous quantities are represented in distributed and overlapping neural populations, with number and size engaging a common parietal spatial code, while size and luminance engage shared occipito-temporal perceptual representations.

Did evolution endow the human brain with a predisposition to represent and acquire knowledge about numbers? Although the parietal lobe has been suggested as a potential substrate for a domain-specific representation of quantities, it is also engaged in verbal, spatial, and attentional functions that may contribute to calculation. To clarify the organisation of number-related processes in the parietal lobe, we examine the three-dimensional intersection of fMRI activations during various numerical tasks, and also review the corresponding neuropsychological evidence. On this basis, we propose a tentative tripartite organisation. The horizontal segment of the intraparietal sulcus (HIPS) appears as a plausible candidate for domain specificity: It is systematically activated whenever numbers are manipulated, independently of number notation, and with increasing activation as the task puts greater emphasis on quantity processing. Depending on task demands, we speculate that this core quantity system, analogous to an internal "number line," can be supplemented by two other circuits. A left angular gyrus area, in connection with other left-hemispheric perisylvian areas, supports the manipulation of numbers in verbal form. Finally, a bilateral posterior superior parietal system supports attentional orientation on the mental number line, just like on any other spatial dimension.

Neuropsychological studies have revealed different subtypes of dyscalculia, including dissociations between exact calculation and approximation abilities, and an impact of number size on performance. To understand the origins of these effects, we measured cerebral activity with functional MRI at 3 Tesla and event-related potentials while healthy volunteers performed exact and approximate calculation tasks with small and large numbers. Bilateral intraparietal, precentral, dorsolateral and superior prefrontal regions showed greater activation during approximation, while the left inferior prefrontal cortex and the bilateral angular regions were more activated during exact calculation. Increasing number size during exact calculation led to increased activation in the same bilateral intraparietal regions as during approximation, as well the left inferior and superior frontal gyri. Event-related potentials gave access to the temporal dynamics of calculation processes, showing that effects of task and of number size could be found as early as 200-300 ms following problem presentation. Altogether, the results reveal two cerebral networks for number processing. Rote arithmetic operations with small numbers have a greater reliance on left-lateralized regions, presumably encoding numbers in verbal format. Approximation and exact calculation with large numbers, however, put heavier emphasis on the left and right parietal cortices, which may encode numbers in a non-verbal quantity format. Subtypes of dyscalculia can be explained by lesions disproportionately affecting only one of these networks.

Cerebral activity during number comparison was studied with functional magnetic resonance imaging using an event-related design. We identified an extended network of task-related areas that showed a phasic activation following each trial, including anterior cingulate, bilateral sensorimotor areas, inferior occipito-temporal cortices, posterior parietal cortices, inferior and dorsolateral prefrontal cortices, and thalami. We then tested which of these areas were affected by number notation, numerical distance and response side, three variables that specifically target processes of visual identification, quantity manipulation and motor response in a serial-stage model of the number comparison task. Our results confirm the role of the right fusiform gyrus in digit identification processes, and of the inferior parietal lobule in the internal manipulation of numerical quantities.